The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

Exploring rich environments and evaluating one's actions without prior knowledge is
immensely challenging. In this paper, we propose Motif, a general method to interface such …

Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian noise fail to
explore efficiently in some reinforcement learning tasks and yet, they perform well in many …

Existing approaches for improving generalization in deep reinforcement learning (RL) have
mostly focused on representation learning, neglecting RL-specific aspects such as …

Reasoning at multiple levels of temporal abstraction is one of the key attributes of
intelligence. In reinforcement learning, this is often modeled through temporally extended …

This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement
learning and sequential decision making, covering value-based RL, policy-gradient …

The use of unmanned aerial vehicles (UAVS) has been suggested as a potential
communications alternative due to their fast implantation, which makes this resource an …

Selecting exploratory actions that generate a rich stream of experience for better learning is
a fundamental challenge in reinforcement learning (RL). An approach to tackle this problem …

Reinforcement learning is a powerful approach to learn behaviour through interactions with
an environment. However, behaviours are usually learned in a purely reactive fashion …

Abstract The combination of Reinforcement Learning (RL) with deep learning has led to a
series of impressive feats, with many believing (deep) RL provides a path towards generally …